Aside

Contact

rafaelulima@gmail.com

GitHub

LinkedIn

Technical Skills

R (tidyverse, tidymodels, shiny)

Python (pandas, dash plotly, sklearn)

SQL

Git

Markdown, Rmarkdown

Languages

English: Fluent

Portuguese: Fluent

Disclaimer

Download PDF.

Made with the R packages pagedown and datadrivencv.

Source code available on GitHub.

Last updated on 2022-11-09.

Main

Rafael Uchoa de Lima

Education

Post-graduation in Data Science and Decision

Insper

São Paulo, Brazil

2022 - 2021

Bachelor of Science in Computer Science

University of Maryland

College Park, Maryland, USA

2019 - 2014

  • Design Cultures & Creativity Honors Program

Professional Experience

Data Scientist

MindMiners

São Paulo, Brazil

Current - 2019

  • Built interactive dashboards for visualization of data collected by client-made surveys, using Dash, Plotly and Pandas;
  • Analyzed app usage metrics and the company’s internal performance data, using SQL and R;
  • Created Machine Learning models for text classification and sentiment analysis;

Volunteer Teaching Assistant

Girls Who Code - University of Maryland

College Park, Maryland, USA

2019 - 2018

  • Taught high school and middle school girls the fundamentals of programming, using Python;

Relevant Coursework

Data Science: Análise Exploratória de Dados

Insper

São Paulo, Brazil

2020 - 2020

  • Studied a framework of data exploration involving analysis, transformation and visualization
  • Analyzed and extracted patterns from real world data, contributing to a more efficient decision-making process;
  • Created dashboard to present data analysis reports, using data visualization tools;

Data Structures

University of Maryland

College Park, Maryland, USA

2018 - 2018

  • Implemented efficient, 1-dimensional data structures, including AVL Trees, Hash Tables and Suffix Arrays;
  • Implemented multi-dimensional data structures, including KD-Trees and Quad-Trees;
  • Studied the LZW Algorithm for lossless data compression;

Design and Analysis of Computer Algorithms

University of Maryland

College Park, Maryland, USA

2017 - 2017

  • Implemented graph and greedy algorithms, including algorithms for bipartiteness, topological sorting, scheduling and minimal spanning trees;
  • Studied dynamic programming for problems such as sequence alignment and shortest paths;
  • Studied randomized algorithms such as minimum cut and game tree evaluation;

Introduction to Machine Learning

University of Maryland

College Park, Maryland, USA

2017 - 2017

  • Implemented different approaches to Machine Learning, including Linear Separators and Neural Networks;
  • Studied Principal Component Analysis and probabilistic models, including the Bernoulli Model and Logistic Regression;
  • Studied Machine Learning theory, including computational learning theory and PAC efficiency;

Disclaimer

Download PDF.

Made with the R packages pagedown and datadrivencv.

Source code available on GitHub.

Last updated on 2022-11-09.